The Experts below are selected from a list of 8922 Experts worldwide ranked by ideXlab platform
H Leib - One of the best experts on this subject based on the ideXlab platform.
-
Shannon Capacity regions for orthogonally multiplexed mimo broadcast channels with informed transmitters
Wireless Communications and Networking Conference, 2004Co-Authors: M Kassouf, H LeibAbstract:This paper considers the Shannon Capacity region of a space and time dispersive multiple-input multiple-output (MIMO) broadcast channel with multi-dimensional space-time modulation. We assume no inter-user cooperation and consider a non-fading environment with orthogonally multiplexed users. With the single user Capacity being achieved by water-filling power allocation and eigen-beamforming transmission, we investigate the Capacity region when each user channel is known at both the transmitter and receiver. Furthermore, the high and low SNR asymptotic behaviour of the Capacity region is studied along with the transmit power allocation that maximizes the sum Capacity. Numerical results show that the Capacity region expands with the number of signal propagation paths, and also with the number of antennas. The effect of space-time modulation is also pointed out.
-
WCNC - Shannon Capacity regions for orthogonally multiplexed MIMO broadcast channels with informed transmitters
2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733), 2004Co-Authors: M Kassouf, H LeibAbstract:This paper considers the Shannon Capacity region of a space and time dispersive multiple-input multiple-output (MIMO) broadcast channel with multi-dimensional space-time modulation. We assume no inter-user cooperation and consider a non-fading environment with orthogonally multiplexed users. With the single user Capacity being achieved by water-filling power allocation and eigen-beamforming transmission, we investigate the Capacity region when each user channel is known at both the transmitter and receiver. Furthermore, the high and low SNR asymptotic behaviour of the Capacity region is studied along with the transmit power allocation that maximizes the sum Capacity. Numerical results show that the Capacity region expands with the number of signal propagation paths, and also with the number of antennas. The effect of space-time modulation is also pointed out.
-
Shannon Capacity and eigen beamforming for space dispersive multipath mimo channels
Wireless Communications and Networking Conference, 2003Co-Authors: M Kassouf, H LeibAbstract:This paper considers the information transfer Capacity of a space dispersive multipath channel with multiple antenna at the transmitter and receiver. The Shannon Capacity is evaluated for such multiple-input multiple-output (MIMO) channels with multi-dimensional space-time modulation. Assuming a non-fading environment and a known channel at the transmitter and receiver, we derive the Shannon Capacity under a transmit average power constraint. It is shown that the signal structure achieving Capacity corresponds to eigen-beamforming. Capacity is shown to increase with the number of signal propagation paths. The effect of the space-time modulation format on the information-theoretic Capacity is also pointed out.
-
WCNC - Shannon Capacity and eigen-beamforming for space dispersive multipath MIMO channels
2003 IEEE Wireless Communications and Networking 2003. WCNC 2003., 1Co-Authors: M Kassouf, H LeibAbstract:This paper considers the information transfer Capacity of a space dispersive multipath channel with multiple antenna at the transmitter and receiver. The Shannon Capacity is evaluated for such multiple-input multiple-output (MIMO) channels with multi-dimensional space-time modulation. Assuming a non-fading environment and a known channel at the transmitter and receiver, we derive the Shannon Capacity under a transmit average power constraint. It is shown that the signal structure achieving Capacity corresponds to eigen-beamforming. Capacity is shown to increase with the number of signal propagation paths. The effect of the space-time modulation format on the information-theoretic Capacity is also pointed out.
M Kassouf - One of the best experts on this subject based on the ideXlab platform.
-
Shannon Capacity regions for orthogonally multiplexed mimo broadcast channels with informed transmitters
Wireless Communications and Networking Conference, 2004Co-Authors: M Kassouf, H LeibAbstract:This paper considers the Shannon Capacity region of a space and time dispersive multiple-input multiple-output (MIMO) broadcast channel with multi-dimensional space-time modulation. We assume no inter-user cooperation and consider a non-fading environment with orthogonally multiplexed users. With the single user Capacity being achieved by water-filling power allocation and eigen-beamforming transmission, we investigate the Capacity region when each user channel is known at both the transmitter and receiver. Furthermore, the high and low SNR asymptotic behaviour of the Capacity region is studied along with the transmit power allocation that maximizes the sum Capacity. Numerical results show that the Capacity region expands with the number of signal propagation paths, and also with the number of antennas. The effect of space-time modulation is also pointed out.
-
WCNC - Shannon Capacity regions for orthogonally multiplexed MIMO broadcast channels with informed transmitters
2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733), 2004Co-Authors: M Kassouf, H LeibAbstract:This paper considers the Shannon Capacity region of a space and time dispersive multiple-input multiple-output (MIMO) broadcast channel with multi-dimensional space-time modulation. We assume no inter-user cooperation and consider a non-fading environment with orthogonally multiplexed users. With the single user Capacity being achieved by water-filling power allocation and eigen-beamforming transmission, we investigate the Capacity region when each user channel is known at both the transmitter and receiver. Furthermore, the high and low SNR asymptotic behaviour of the Capacity region is studied along with the transmit power allocation that maximizes the sum Capacity. Numerical results show that the Capacity region expands with the number of signal propagation paths, and also with the number of antennas. The effect of space-time modulation is also pointed out.
-
Shannon Capacity and eigen beamforming for space dispersive multipath mimo channels
Wireless Communications and Networking Conference, 2003Co-Authors: M Kassouf, H LeibAbstract:This paper considers the information transfer Capacity of a space dispersive multipath channel with multiple antenna at the transmitter and receiver. The Shannon Capacity is evaluated for such multiple-input multiple-output (MIMO) channels with multi-dimensional space-time modulation. Assuming a non-fading environment and a known channel at the transmitter and receiver, we derive the Shannon Capacity under a transmit average power constraint. It is shown that the signal structure achieving Capacity corresponds to eigen-beamforming. Capacity is shown to increase with the number of signal propagation paths. The effect of the space-time modulation format on the information-theoretic Capacity is also pointed out.
-
WCNC - Shannon Capacity and eigen-beamforming for space dispersive multipath MIMO channels
2003 IEEE Wireless Communications and Networking 2003. WCNC 2003., 1Co-Authors: M Kassouf, H LeibAbstract:This paper considers the information transfer Capacity of a space dispersive multipath channel with multiple antenna at the transmitter and receiver. The Shannon Capacity is evaluated for such multiple-input multiple-output (MIMO) channels with multi-dimensional space-time modulation. Assuming a non-fading environment and a known channel at the transmitter and receiver, we derive the Shannon Capacity under a transmit average power constraint. It is shown that the signal structure achieving Capacity corresponds to eigen-beamforming. Capacity is shown to increase with the number of signal propagation paths. The effect of the space-time modulation format on the information-theoretic Capacity is also pointed out.
Ofer Shayevitz - One of the best experts on this subject based on the ideXlab platform.
-
the interactive Capacity of the binary symmetric channel is at least 1 40 the Shannon Capacity
International Symposium on Information Theory, 2019Co-Authors: Assaf Benyishai, Young-han Kim, Or Ordentlich, Ofer ShayevitzAbstract:We define the interactive Capacity of the binary symmetric channel (BSC) as the maximal rate for which any interactive protocol can be fully and reliably simulated over a pair of BSC’s. We show that this quantity is at least 1/40 of the BSC Shannon Capacity, uniformly for all channel crossover probabilities. Our result is based on a public-coin rewind-if-error coding scheme in the spirit of Kol & Raz 2013 [1].
-
ISIT - The Interactive Capacity of the Binary Symmetric Channel is at Least 1/40 the Shannon Capacity
2019 IEEE International Symposium on Information Theory (ISIT), 2019Co-Authors: Assaf Ben-yishai, Young-han Kim, Or Ordentlich, Ofer ShayevitzAbstract:We define the interactive Capacity of the binary symmetric channel (BSC) as the maximal rate for which any interactive protocol can be fully and reliably simulated over a pair of BSC’s. We show that this quantity is at least 1/40 of the BSC Shannon Capacity, uniformly for all channel crossover probabilities. Our result is based on a public-coin rewind-if-error coding scheme in the spirit of Kol & Raz 2013 [1].
-
a bound on the Shannon Capacity via a linear programming variation
SIAM Journal on Discrete Mathematics, 2018Co-Authors: Itzhak Tamo, Ofer ShayevitzAbstract:We prove an upper bound on the Shannon Capacity of a graph via a linear programming variation. We show that our bound can outperform both the Lovasz theta number and the Haemers minimum rank bound....
-
A Bound on the Shannon Capacity via a Linear Programming Variation
SIAM Journal on Discrete Mathematics, 2018Co-Authors: Itzhak Tamo, Ofer ShayevitzAbstract:We prove an upper bound on the Shannon Capacity of a graph via a linear programming variation. We show that our bound can outperform both the Lov\'asz theta number and the Haemers minimum rank bound. As a by-product, we also obtain a new upper bound on the broadcast rate of Index Coding.
-
Shannon Capacity is Achievable for Binary Interactive First-Order Markovian Protocols.
arXiv: Information Theory, 2017Co-Authors: Assaf Ben-yishai, Ofer Shayevitz, Young-han KimAbstract:We address the problem of simulating an arbitrary binary interactive first-order Markovian protocol over a pair of binary symmetric channels with crossover probability $\varepsilon$. We are interested in the achievable rates of reliable simulation, i.e., in characterizing the smallest possible blowup in communications such that a vanishing error probability (in the protocol length) can be attained. Whereas for general interactive protocols the output of each party may depend on all previous outputs of its counterpart, in a (first-order) Markovian protocol this dependence is limited to the last observed output only. In this paper we prove that the one-way Shannon Capacity, $1-h(\varepsilon)$, can be achieved for any binary first-order Markovian protocol. This surprising result, is to the best of our knowledge, the first example in which non-trivial interactive protocol can be simulated in the Shannon Capacity. Our scheme is based on two simple notions: non-interactive simulation, block-wise interactive communication. Previous results in the field discuss different families of protocol and mostly assess the achievable rates at the limit where $\varepsilon\to0$. We also show that for higher order Markovian protocols, if the transmission functions are drawn uniformly i.i.d, the probability of drawing a non-Capacity achieving protocol goes to zero with $n$.
Michelle Effros - One of the best experts on this subject based on the ideXlab platform.
-
on the equivalence of Shannon Capacity and stable Capacity in networks with memoryless channels
International Symposium on Information Theory, 2011Co-Authors: Hongyi Yao, Michelle EffrosAbstract:An equivalence result is established between the Shannon Capacity and the stable Capacity of communication networks. Given a discrete-time network with memoryless, time-invariant, discrete-output channels, it is proved that the Shannon Capacity equals the stable Capacity. The results treat general demands (e.g., multiple unicast demands) and apply even when neither the Shannon Capacity nor the stable Capacity is known for the given demands. The result also generalize from discrete-alphabet channels to Gaussian channels.
-
ISIT - On the equivalence of Shannon Capacity and stable Capacity in networks with memoryless channels
2011 IEEE International Symposium on Information Theory Proceedings, 2011Co-Authors: Hongyi Yao, Michelle EffrosAbstract:An equivalence result is established between the Shannon Capacity and the stable Capacity of communication networks. Given a discrete-time network with memoryless, time-invariant, discrete-output channels, it is proved that the Shannon Capacity equals the stable Capacity. The results treat general demands (e.g., multiple unicast demands) and apply even when neither the Shannon Capacity nor the stable Capacity is known for the given demands. The result also generalize from discrete-alphabet channels to Gaussian channels.
Hongyi Yao - One of the best experts on this subject based on the ideXlab platform.
-
on the equivalence of Shannon Capacity and stable Capacity in networks with memoryless channels
International Symposium on Information Theory, 2011Co-Authors: Hongyi Yao, Michelle EffrosAbstract:An equivalence result is established between the Shannon Capacity and the stable Capacity of communication networks. Given a discrete-time network with memoryless, time-invariant, discrete-output channels, it is proved that the Shannon Capacity equals the stable Capacity. The results treat general demands (e.g., multiple unicast demands) and apply even when neither the Shannon Capacity nor the stable Capacity is known for the given demands. The result also generalize from discrete-alphabet channels to Gaussian channels.
-
ISIT - On the equivalence of Shannon Capacity and stable Capacity in networks with memoryless channels
2011 IEEE International Symposium on Information Theory Proceedings, 2011Co-Authors: Hongyi Yao, Michelle EffrosAbstract:An equivalence result is established between the Shannon Capacity and the stable Capacity of communication networks. Given a discrete-time network with memoryless, time-invariant, discrete-output channels, it is proved that the Shannon Capacity equals the stable Capacity. The results treat general demands (e.g., multiple unicast demands) and apply even when neither the Shannon Capacity nor the stable Capacity is known for the given demands. The result also generalize from discrete-alphabet channels to Gaussian channels.
